diff --git a/src/TensorFlowNET.Core/APIs/tf.random.cs b/src/TensorFlowNET.Core/APIs/tf.random.cs
new file mode 100644
index 00000000..4ec40d11
--- /dev/null
+++ b/src/TensorFlowNET.Core/APIs/tf.random.cs
@@ -0,0 +1,26 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow
+{
+ public static partial class tf
+ {
+ ///
+ /// Outputs random values from a normal distribution.
+ ///
+ ///
+ ///
+ ///
+ ///
+ ///
+ ///
+ ///
+ public static Tensor random_normal(int[] shape,
+ float mean = 0.0f,
+ float stddev = 1.0f,
+ TF_DataType dtype = TF_DataType.TF_FLOAT,
+ int? seed = null,
+ string name = "") => random_ops.random_normal(shape, mean, stddev, dtype, seed, name);
+ }
+}
diff --git a/src/TensorFlowNET.Core/Framework/random_seed.py.cs b/src/TensorFlowNET.Core/Framework/random_seed.py.cs
new file mode 100644
index 00000000..eb2ea386
--- /dev/null
+++ b/src/TensorFlowNET.Core/Framework/random_seed.py.cs
@@ -0,0 +1,14 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow
+{
+ public class random_seed
+ {
+ public static (int?, int?) get_seed(int? op_seed = null)
+ {
+ return (null, null);
+ }
+ }
+}
diff --git a/src/TensorFlowNET.Core/Graphs/Graph.cs b/src/TensorFlowNET.Core/Graphs/Graph.cs
index a926a57f..79e9fbe5 100644
--- a/src/TensorFlowNET.Core/Graphs/Graph.cs
+++ b/src/TensorFlowNET.Core/Graphs/Graph.cs
@@ -141,10 +141,9 @@ namespace Tensorflow
}
if (String.IsNullOrEmpty(name))
- {
name = op_type;
- }
-
+ // If a names ends with a '/' it is a "name scope" and we use it as-is,
+ // after removing the trailing '/'.
name = name.EndsWith("/") ? ops._name_from_scope_name(name) : unique_name(name);
var node_def = ops._NodeDef(op_type, name, device: "", attrs: attrs);
diff --git a/src/TensorFlowNET.Core/Operations/OpDefLibrary.cs b/src/TensorFlowNET.Core/Operations/OpDefLibrary.cs
index 76dd318a..62b4821a 100644
--- a/src/TensorFlowNET.Core/Operations/OpDefLibrary.cs
+++ b/src/TensorFlowNET.Core/Operations/OpDefLibrary.cs
@@ -14,7 +14,7 @@ namespace Tensorflow
{
public Operation _apply_op_helper(string op_type_name, string name = "", dynamic args = null)
{
- var keywords = ConvertToDict(args);
+ Dictionary keywords = ConvertToDict(args);
var g = ops.get_default_graph();
var op_def = g.GetOpDef(op_type_name);
@@ -42,7 +42,8 @@ namespace Tensorflow
var attrs = new Dictionary();
var inputs = new List();
var input_types = new List();
-
+ dynamic values = null;
+
return Python.with(new ops.name_scope(name), scope =>
{
var inferred_from = new Dictionary();
@@ -53,7 +54,17 @@ namespace Tensorflow
foreach (var input_arg in op_def.InputArg)
{
var input_name = input_arg.Name;
- var values = keywords[input_name];
+
+ if (keywords.ContainsKey(input_name))
+ values = keywords[input_name];
+ else if (keywords.ContainsKey(input_name + "_"))
+ {
+ input_name += "_";
+ values = keywords[input_name];
+ }
+ else
+ throw new TypeError("No argument for input " + input_name);
+
// Goals:
// * Convert values to Tensors if it contains constants.
// * Verify that values is a list if that matches the input_arg's
@@ -92,8 +103,8 @@ namespace Tensorflow
values = ops.internal_convert_n_to_tensor(values,
name: input_arg.Name,
- dtype: dtype,
- preferred_dtype: default_dtype,
+ dtype: dtype.as_tf_dtype(),
+ preferred_dtype: default_dtype.as_tf_dtype(),
as_ref: input_arg.IsRef);
}
else
@@ -107,9 +118,9 @@ namespace Tensorflow
values = ops.internal_convert_to_tensor(values,
name: input_name,
- dtype: dtype,
+ dtype: dtype.as_tf_dtype(),
as_ref: input_arg.IsRef,
- preferred_dtype: default_dtype);
+ preferred_dtype: default_dtype.as_tf_dtype());
//if (!String.IsNullOrEmpty(input_arg.TypeAttr))
//attrs[input_arg.TypeAttr] = values.dtype;
diff --git a/src/TensorFlowNET.Core/Operations/array_ops.py.cs b/src/TensorFlowNET.Core/Operations/array_ops.py.cs
index 28ff42cf..40e9f38f 100644
--- a/src/TensorFlowNET.Core/Operations/array_ops.py.cs
+++ b/src/TensorFlowNET.Core/Operations/array_ops.py.cs
@@ -7,6 +7,8 @@ namespace Tensorflow
{
public class array_ops
{
+ public static Tensor placeholder_with_default(T input, int[] shape, string name = "") => gen_array_ops.placeholder_with_default(input, shape, name);
+
public static Tensor zeros(Shape shape, TF_DataType dtype = TF_DataType.TF_FLOAT, string name = "")
{
dtype = dtype.as_base_dtype();
@@ -35,13 +37,13 @@ namespace Tensorflow
var nd = np.zeros(shape);
if (shape.Size < 1000)
{
- return constant_op.constant(nd, name);
+ return constant_op.constant(nd, name: name);
}
else
{
tShape = constant_op._tensor_shape_tensor_conversion_function(shape.as_shape());
var c = constant_op.constant(0);
- return gen_array_ops.fill(tShape, c, name);
+ return gen_array_ops.fill(tShape, c, name: name);
}
}
@@ -99,7 +101,7 @@ namespace Tensorflow
if (optimize && input_shape.is_fully_defined())
{
var nd = np.array(input_tensor.shape, out_type.as_numpy_datatype());
- return constant_op.constant(nd, name);
+ return constant_op.constant(nd, name: name);
}
}
@@ -122,7 +124,7 @@ namespace Tensorflow
if (input_shape.is_fully_defined())
{
var nd = np.array(input_tensor.shape, out_type.as_numpy_datatype());
- return constant_op.constant(nd, name);
+ return constant_op.constant(nd, name: name);
}
}
diff --git a/src/TensorFlowNET.Core/Operations/gen_array_ops.cs b/src/TensorFlowNET.Core/Operations/gen_array_ops.cs
index b3a3e607..8bf345fc 100644
--- a/src/TensorFlowNET.Core/Operations/gen_array_ops.cs
+++ b/src/TensorFlowNET.Core/Operations/gen_array_ops.cs
@@ -113,7 +113,7 @@ namespace Tensorflow
///
///
///
- public static Tensor placeholder_with_default(T input, TensorShape shape, string name = "")
+ public static Tensor placeholder_with_default(T input, int[] shape, string name = "")
{
var _op = _op_def_lib._apply_op_helper("PlaceholderWithDefault", name, new { input, shape, name });
return _op.outputs[0];
diff --git a/src/TensorFlowNET.Core/Operations/gen_random_ops.py.cs b/src/TensorFlowNET.Core/Operations/gen_random_ops.py.cs
new file mode 100644
index 00000000..643e7d0a
--- /dev/null
+++ b/src/TensorFlowNET.Core/Operations/gen_random_ops.py.cs
@@ -0,0 +1,33 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow
+{
+ public class gen_random_ops
+ {
+ public static OpDefLibrary _op_def_lib = new OpDefLibrary();
+
+ ///
+ /// Outputs random values from a normal distribution.
+ ///
+ ///
+ ///
+ ///
+ ///
+ ///
+ ///
+ public static Tensor random_standard_normal(Tensor shape, TF_DataType dtype = TF_DataType.DtInvalid, int? seed = null, int? seed2 = null, string name = "")
+ {
+ if (!seed.HasValue)
+ seed = 0;
+ if (!seed2.HasValue)
+ seed2 = 0;
+
+ var _op = _op_def_lib._apply_op_helper("RandomStandardNormal", name: name,
+ args: new { shape, dtype, seed, seed2 });
+
+ return _op.outputs[0];
+ }
+ }
+}
diff --git a/src/TensorFlowNET.Core/Operations/math_ops.py.cs b/src/TensorFlowNET.Core/Operations/math_ops.py.cs
index ef273c49..1e78285a 100644
--- a/src/TensorFlowNET.Core/Operations/math_ops.py.cs
+++ b/src/TensorFlowNET.Core/Operations/math_ops.py.cs
@@ -6,6 +6,8 @@ namespace Tensorflow
{
public class math_ops
{
+ public static Tensor add(Tensor x, Tensor y, string name = "") => gen_math_ops.add(x, y, name);
+
public static Tensor cast(Tensor x, TF_DataType dtype = TF_DataType.DtInvalid, string name = "")
{
var base_type = dtype.as_base_dtype();
diff --git a/src/TensorFlowNET.Core/Operations/random_ops.py.cs b/src/TensorFlowNET.Core/Operations/random_ops.py.cs
new file mode 100644
index 00000000..3334a2d9
--- /dev/null
+++ b/src/TensorFlowNET.Core/Operations/random_ops.py.cs
@@ -0,0 +1,35 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow
+{
+ public class random_ops
+ {
+ public static Tensor random_normal(int[] shape,
+ float mean = 0.0f,
+ float stddev = 1.0f,
+ TF_DataType dtype = TF_DataType.TF_FLOAT,
+ int? seed = null,
+ string name = "")
+ {
+ return Python.with(new ops.name_scope(name, "random_normal", new object[] { shape, mean, stddev }), scope =>
+ {
+ var shape_tensor = _ShapeTensor(shape);
+ var mean_tensor = ops.convert_to_tensor(mean, dtype: dtype, name: "mean");
+ var stddev_tensor = ops.convert_to_tensor(stddev, dtype: dtype, name = "stddev");
+ var (seed1, seed2) = random_seed.get_seed(seed);
+ var rnd = gen_random_ops.random_standard_normal(shape_tensor, dtype: dtype, seed: seed1, seed2: seed2);
+ var mul = rnd * stddev_tensor;
+ var value = math_ops.add(mul, mean_tensor, name: name);
+ return value;
+ });
+ }
+
+ private static Tensor _ShapeTensor(int[] shape)
+ {
+ return ops.convert_to_tensor(shape, name: "shape");
+ }
+ }
+}
+
diff --git a/src/TensorFlowNET.Core/Sessions/BaseSession.cs b/src/TensorFlowNET.Core/Sessions/BaseSession.cs
index 5a828029..8044e904 100644
--- a/src/TensorFlowNET.Core/Sessions/BaseSession.cs
+++ b/src/TensorFlowNET.Core/Sessions/BaseSession.cs
@@ -55,11 +55,19 @@ namespace Tensorflow
var subfeed_dtype = subfeed_t.dtype.as_numpy_datatype();
switch(subfeed.Value)
{
+ case float floatVal:
+ feed_dict_tensor[subfeed_t] = (NDArray)floatVal;
+ break;
+ case int intVal:
+ feed_dict_tensor[subfeed_t] = (NDArray)intVal;
+ break;
case string str:
- feed_dict_tensor[subfeed_t] = np.array(str);
- feed_map[subfeed_t.name] = new Tuple(subfeed_t, subfeed.Value);
+ feed_dict_tensor[subfeed_t] = (NDArray)str;
break;
+ default:
+ throw new NotImplementedException("_run subfeed");
}
+ feed_map[subfeed_t.name] = new Tuple(subfeed_t, subfeed.Value);
}
}
diff --git a/src/TensorFlowNET.Core/Tensors/constant_op.cs b/src/TensorFlowNET.Core/Tensors/constant_op.cs
index e1f930f2..f65d71c4 100644
--- a/src/TensorFlowNET.Core/Tensors/constant_op.cs
+++ b/src/TensorFlowNET.Core/Tensors/constant_op.cs
@@ -19,7 +19,12 @@ namespace Tensorflow
/// Optional name for the tensor.
/// Boolean that enables verification of a shape of values.
///
- public static Tensor constant(NDArray nd, string name = "Const", bool verify_shape = false)
+ public static Tensor constant(object value, TF_DataType dtype = TF_DataType.DtInvalid, int[] shape = null, string name = "Const")
+ {
+ return _constant_impl(value, dtype, shape, name, verify_shape: false, allow_broadcast: true);
+ }
+
+ private static Tensor _constant_impl(object value, TF_DataType dtype, int[] shape, string name, bool verify_shape, bool allow_broadcast)
{
if (tf.context.executing_eagerly())
{
@@ -27,13 +32,13 @@ namespace Tensorflow
}
Graph g = ops.get_default_graph();
- var tensor_pb = tensor_util.make_tensor_proto(nd, verify_shape);
- var tensor_value = new AttrValue
- {
- Type = tensor_pb.Dtype,
- Tensor = tensor_pb
- };
-
+ var tensor_value = new AttrValue();
+ tensor_value.Tensor = tensor_util.make_tensor_proto(value,
+ dtype: dtype,
+ shape: shape,
+ verify_shape: verify_shape,
+ allow_broadcast: allow_broadcast);
+
var dtype_value = new AttrValue
{
Type = tensor_value.Tensor.Dtype,
@@ -44,8 +49,8 @@ namespace Tensorflow
attrs["dtype"] = dtype_value;
var op = g.create_op("Const",
- null,
- new TF_DataType[] { (TF_DataType)dtype_value.Type },
+ new Tensor[0],
+ new TF_DataType[] { dtype_value.Type.as_tf_dtype() },
attrs: attrs,
name: name);
@@ -81,7 +86,7 @@ namespace Tensorflow
if (string.IsNullOrEmpty(name))
name = "shape_as_tensor";
- return constant_op.constant(s_list, name);
+ return constant_op.constant(s_list, name: name);
}
}
}
diff --git a/src/TensorFlowNET.Core/Tensors/dtypes.cs b/src/TensorFlowNET.Core/Tensors/dtypes.cs
index 31bfe3e2..4c0bd693 100644
--- a/src/TensorFlowNET.Core/Tensors/dtypes.cs
+++ b/src/TensorFlowNET.Core/Tensors/dtypes.cs
@@ -83,6 +83,20 @@ namespace Tensorflow
type;
}
+ public static TF_DataType as_tf_dtype(this DataType type)
+ {
+ TF_DataType dtype = TF_DataType.DtInvalid;
+
+ switch (type)
+ {
+ default:
+ Enum.TryParse(((int)type).ToString(), out dtype);
+ break;
+ }
+
+ return dtype;
+ }
+
public static TF_DataType as_ref(this TF_DataType type)
{
return (int)type < 100 ?
diff --git a/src/TensorFlowNET.Core/Tensors/tensor_util.cs b/src/TensorFlowNET.Core/Tensors/tensor_util.cs
index 59aa3639..8bdac8d2 100644
--- a/src/TensorFlowNET.Core/Tensors/tensor_util.cs
+++ b/src/TensorFlowNET.Core/Tensors/tensor_util.cs
@@ -10,33 +10,166 @@ namespace Tensorflow
{
public static class tensor_util
{
- public static TensorProto make_tensor_proto(NDArray nd, bool verify_shape = false)
+ public static TF_DataType[] _TENSOR_CONTENT_TYPES =
{
- var shape = nd.Storage.Shape;
+ TF_DataType.TF_FLOAT, TF_DataType.TF_DOUBLE, TF_DataType.TF_INT32, TF_DataType.TF_UINT8, TF_DataType.TF_INT16,
+ TF_DataType.TF_INT8, TF_DataType.TF_INT64, TF_DataType.TF_QINT8, TF_DataType.TF_QUINT8, TF_DataType.TF_QINT16,
+ TF_DataType.TF_QUINT16, TF_DataType.TF_QINT32, TF_DataType.TF_UINT32, TF_DataType.TF_UINT64
+ };
+
+ ///
+ /// Create a TensorProto.
+ ///
+ ///
+ ///
+ ///
+ ///
+ ///
+ ///
+ public static TensorProto make_tensor_proto(object values, TF_DataType dtype = TF_DataType.DtInvalid, int[] shape = null, bool verify_shape = false, bool allow_broadcast = false)
+ {
+ if (allow_broadcast && verify_shape)
+ throw new ValueError("allow_broadcast and verify_shape are not both allowed.");
+ if (values is TensorProto tp)
+ return tp;
+
+ if (dtype != TF_DataType.DtInvalid)
+ ;
+
+ bool is_quantized = new TF_DataType[]
+ {
+ TF_DataType.TF_QINT8, TF_DataType.TF_QUINT8, TF_DataType.TF_QINT16, TF_DataType.TF_QUINT16,
+ TF_DataType.TF_QINT32
+ }.Contains(dtype);
+
+ // We first convert value to a numpy array or scalar.
+ NDArray nparray = null;
+
+ if (values is NDArray nd)
+ {
+ nparray = nd;
+ }
+ else
+ {
+ if (values == null)
+ throw new ValueError("None values not supported.");
+
+ switch (values)
+ {
+ /*case bool boolVal:
+ nparray = boolVal;
+ break;*/
+ case int intVal:
+ nparray = intVal;
+ break;
+ case int[] intVals:
+ nparray = np.array(intVals);
+ break;
+ case float floatVal:
+ nparray = floatVal;
+ break;
+ case double doubleVal:
+ nparray = doubleVal;
+ break;
+ case string strVal:
+ nparray = strVal;
+ break;
+ default:
+ throw new Exception("make_tensor_proto Not Implemented");
+ }
+ }
+
+ var numpy_dtype = dtypes.as_dtype(nparray.dtype);
+ if (numpy_dtype == TF_DataType.DtInvalid)
+ throw new TypeError($"Unrecognized data type: {nparray.dtype}");
+
+ // If dtype was specified and is a quantized type, we convert
+ // numpy_dtype back into the quantized version.
+ if (is_quantized)
+ numpy_dtype = dtype;
+
+ bool is_same_size = false;
+ int shape_size = 0;
+
+ // If shape is not given, get the shape from the numpy array.
+ if (shape == null)
+ {
+ shape = nparray.shape;
+ is_same_size = true;
+ shape_size = nparray.size;
+ }
+ else
+ {
+ throw new NotImplementedException("make_tensor_proto shape not implemented");
+ }
- var numpy_dtype = dtypes.as_dtype(nd.dtype);
var tensor_proto = new tensor_pb2.TensorProto
{
Dtype = numpy_dtype.as_datatype_enum(),
- TensorShape = shape.reshape(nd.shape).as_proto()
+ TensorShape = tensor_util.as_shape(shape)
};
- switch (nd.dtype.Name)
+ if (is_same_size && _TENSOR_CONTENT_TYPES.Contains(numpy_dtype) && shape_size > 1)
+ {
+ var bytes = new List();
+ var nd2 = nparray.ravel();
+ switch (nparray.dtype.Name)
+ {
+ case "Int32":
+ nd2.Data().Select(x =>
+ {
+ bytes.AddRange(BitConverter.GetBytes(x));
+ return x;
+ }).ToArray();
+ break;
+ case "Single":
+ nd2.Data().Select(x =>
+ {
+ bytes.AddRange(BitConverter.GetBytes(x));
+ return x;
+ }).ToArray();
+ break;
+ case "Double":
+ nd2.Data().Select(x =>
+ {
+ bytes.AddRange(BitConverter.GetBytes(x));
+ return x;
+ }).ToArray();
+ break;
+ default:
+ throw new Exception("make_tensor_proto Not Implemented");
+ }
+ tensor_proto.TensorContent = Google.Protobuf.ByteString.CopyFrom(bytes.ToArray());
+ return tensor_proto;
+ }
+
+ if (numpy_dtype == TF_DataType.TF_STRING && !(values is NDArray) && values is string str)
{
+ tensor_proto.StringVal.Add(Google.Protobuf.ByteString.CopyFromUtf8(str));
+ return tensor_proto;
+ }
+
+ var proto_values = nparray.ravel();
+
+ switch (nparray.dtype.Name)
+ {
+ case "Bool":
+ tensor_proto.BoolVal.AddRange(proto_values.Data());
+ break;
case "Int32":
- tensor_proto.IntVal.AddRange(nd.Data());
+ tensor_proto.IntVal.AddRange(proto_values.Data());
break;
case "Single":
- tensor_proto.FloatVal.AddRange(nd.Data());
+ tensor_proto.FloatVal.AddRange(proto_values.Data());
break;
case "Double":
- tensor_proto.DoubleVal.AddRange(nd.Data());
+ tensor_proto.DoubleVal.AddRange(proto_values.Data());
break;
case "String":
- tensor_proto.StringVal.AddRange(nd.Data().Select(x => Google.Protobuf.ByteString.CopyFromUtf8(x)));
+ tensor_proto.StringVal.AddRange(proto_values.Data().Select(x => Google.Protobuf.ByteString.CopyFromUtf8(x.ToString())));
break;
default:
- throw new Exception("Not Implemented");
+ throw new Exception("make_tensor_proto Not Implemented");
}
return tensor_proto;
@@ -73,14 +206,24 @@ namespace Tensorflow
return nd;
}
- public static TensorShapeProto as_shape(long[] dims)
+ public static TensorShapeProto as_shape(T[] dims)
{
TensorShapeProto shape = new TensorShapeProto();
for (int i = 0; i < dims.Length; i++)
{
var dim = new TensorShapeProto.Types.Dim();
- dim.Size = dims[i];
+ switch(dims[i])
+ {
+ case int n:
+ dim.Size = n;
+ break;
+ case long l:
+ dim.Size = l;
+ break;
+ default:
+ throw new NotImplementedException("as_shape Not Implemented");
+ }
dim.Name = $"dim_{i}";
shape.Dim.Add(dim);
diff --git a/src/TensorFlowNET.Core/Tensors/tf.constant.cs b/src/TensorFlowNET.Core/Tensors/tf.constant.cs
index 26711316..93bbc459 100644
--- a/src/TensorFlowNET.Core/Tensors/tf.constant.cs
+++ b/src/TensorFlowNET.Core/Tensors/tf.constant.cs
@@ -7,14 +7,8 @@ namespace Tensorflow
{
public static partial class tf
{
- public static Tensor constant(NDArray nd, string name = "Const", bool verify_shape = false)
- {
- return constant_op.constant(nd, name, verify_shape);
- }
+ public static Tensor constant(NDArray nd, string name = "Const") => constant_op.constant(nd, name: name);
- public static Tensor zeros(Shape shape, TF_DataType dtype = TF_DataType.TF_FLOAT, string name = "")
- {
- return array_ops.zeros(shape, dtype, name);
- }
+ public static Tensor zeros(Shape shape, TF_DataType dtype = TF_DataType.TF_FLOAT, string name = "") => array_ops.zeros(shape, dtype, name);
}
}
diff --git a/src/TensorFlowNET.Core/Train/Saving/BaseSaverBuilder.cs b/src/TensorFlowNET.Core/Train/Saving/BaseSaverBuilder.cs
index 7ce6987a..0c7875f9 100644
--- a/src/TensorFlowNET.Core/Train/Saving/BaseSaverBuilder.cs
+++ b/src/TensorFlowNET.Core/Train/Saving/BaseSaverBuilder.cs
@@ -84,8 +84,8 @@ namespace Tensorflow
name = scope;
// Add a placeholder string tensor for the filename.
- var filename_tensor = gen_array_ops.placeholder_with_default( string.IsNullOrEmpty(filename) ? "model" : filename, shape: new TensorShape(), name: "filename");
- filename_tensor = gen_array_ops.placeholder_with_default(filename_tensor, shape: new TensorShape(), name: "Const");
+ var filename_tensor = array_ops.placeholder_with_default(string.IsNullOrEmpty(filename) ? "model" : filename, shape: new int[0], name: "filename");
+ filename_tensor = gen_array_ops.placeholder_with_default(filename_tensor, shape: new int[0], name: "Const");
// Keep the name "Const" for backwards compatibility.
// Add the save ops.
diff --git a/src/TensorFlowNET.Core/ops.py.cs b/src/TensorFlowNET.Core/ops.py.cs
index e43881c1..74575cff 100644
--- a/src/TensorFlowNET.Core/ops.py.cs
+++ b/src/TensorFlowNET.Core/ops.py.cs
@@ -68,16 +68,14 @@ namespace Tensorflow
///
///
///
- public static Tensor convert_to_tensor(object value, TF_DataType dtype = TF_DataType.DtInvalid, string name = "")
+ public static Tensor convert_to_tensor(object value, TF_DataType dtype = TF_DataType.DtInvalid, string name = "", TF_DataType preferred_dtype = TF_DataType.DtInvalid)
{
- switch (value)
- {
- case Tensor val:
- return val;
- default:
- var nd = tensor_util.convert_to_numpy_ndarray(value);
- return constant_op.constant(nd, name);
- }
+ return convert_to_tensor_v2(value, dtype, preferred_dtype, name);
+ }
+
+ public static Tensor convert_to_tensor_v2(object value, TF_DataType dtype = TF_DataType.DtInvalid, TF_DataType dtype_hint = TF_DataType.DtInvalid, string name = "")
+ {
+ return internal_convert_to_tensor(value, dtype: dtype, name: name, preferred_dtype: dtype_hint, as_ref: false);
}
public static Tensor convert_to_tensor_or_composite(Tensor value, TF_DataType dtype = TF_DataType.DtInvalid, string name = "")
@@ -87,7 +85,7 @@ namespace Tensorflow
public static Tensor internal_convert_to_tensor_or_composite(Tensor value, TF_DataType dtype = TF_DataType.DtInvalid, string name = "", bool as_ref = false)
{
- return internal_convert_to_tensor(value, dtype: dtype.as_datatype_enum(), name: name, as_ref: as_ref);
+ return internal_convert_to_tensor(value, dtype: dtype, name: name, as_ref: as_ref);
}
///
@@ -117,17 +115,14 @@ namespace Tensorflow
var op_desc = graph.NewOperation(node_def.Op, node_def.Name);
// Add inputs
- if(inputs != null)
+ foreach (var op_input in inputs)
{
- foreach (var op_input in inputs)
- {
- if (op_input is Tensor[] op_inputs)
- c_api.TF_AddInputList(op_desc, op_inputs.Select(x => x._as_tf_output()).ToArray(), op_inputs.Length);
- else if (op_input is Tensor op_input1)
- c_api.TF_AddInput(op_desc, op_input1._as_tf_output());
- else
- throw new NotImplementedException("_create_c_op");
- }
+ if (op_input is Tensor[] op_inputs)
+ c_api.TF_AddInputList(op_desc, op_inputs.Select(x => x._as_tf_output()).ToArray(), op_inputs.Length);
+ else if (op_input is Tensor op_input1)
+ c_api.TF_AddInput(op_desc, op_input1._as_tf_output());
+ else
+ throw new NotImplementedException("_create_c_op");
}
var status = new Status();
@@ -142,8 +137,8 @@ namespace Tensorflow
var bytes = attr.Value.ToByteArray();
var proto = Marshal.AllocHGlobal(bytes.Length);
Marshal.Copy(bytes, 0, proto, bytes.Length);
-
- c_api.TF_SetAttrValueProto(op_desc, attr.Key, proto, proto_len: (uint)bytes.Length, status: status);
+ uint len = (uint)bytes.Length;
+ c_api.TF_SetAttrValueProto(op_desc, attr.Key, proto, proto_len: len, status: status);
status.Check(true);
}
@@ -385,8 +380,8 @@ namespace Tensorflow
return ret.ToArray();
}
- public static Tensor[] internal_convert_n_to_tensor(T[] values, DataType dtype = DataType.DtInvalid,
- string name = "", DataType preferred_dtype = DataType.DtInvalid,
+ public static Tensor[] internal_convert_n_to_tensor(T[] values, TF_DataType dtype = TF_DataType.DtInvalid,
+ string name = "", TF_DataType preferred_dtype = TF_DataType.DtInvalid,
bool as_ref = false)
{
var ret = new List();
@@ -400,28 +395,30 @@ namespace Tensorflow
return ret.ToArray();
}
- public static Tensor internal_convert_to_tensor(T value, DataType dtype = DataType.DtInvalid,
- string name = "", DataType preferred_dtype = DataType.DtInvalid,
+ public static Tensor internal_convert_to_tensor(object value, TF_DataType dtype = TF_DataType.DtInvalid,
+ string name = "", TF_DataType preferred_dtype = TF_DataType.DtInvalid,
bool as_ref = false)
{
- switch (typeof(T).Name)
+ switch (value)
{
- case "Tensor":
- return value as Tensor;
- case "String":
- return constant_op.constant(Convert.ToString(value), name);
- case "String[]":
- return constant_op.constant(value as string[], name);
- case "Int32":
- return constant_op.constant(Convert.ToInt32(value), name);
- case "Single":
- return constant_op.constant(Convert.ToSingle(value), name);
- case "Double":
- return constant_op.constant(Convert.ToDouble(value), name);
- case "RefVariable":
- return (value as RefVariable)._TensorConversionFunction(as_ref: as_ref);
+ case Tensor tensor:
+ return tensor;
+ case string str:
+ return constant_op.constant(str, dtype: dtype, name: name);
+ case string[] strArray:
+ return constant_op.constant(strArray, dtype: dtype, name: name);
+ case int intVal:
+ return constant_op.constant(intVal, dtype: dtype, name: name);
+ case int[] intArray:
+ return constant_op.constant(intArray, dtype: dtype, name: name);
+ case float floatVal:
+ return constant_op.constant(floatVal, dtype: dtype, name: name);
+ case double doubleVal:
+ return constant_op.constant(doubleVal, dtype: dtype, name: name);
+ case RefVariable varVal:
+ return varVal._TensorConversionFunction(as_ref: as_ref);
default:
- throw new NotImplementedException($"internal_convert_to_tensor: Can't convert {typeof(T).Name} to Tensor");
+ throw new NotImplementedException($"internal_convert_to_tensor: Can't convert {value.GetType().Name} to Tensor");
}
}
}
diff --git a/test/TensorFlowNET.UnitTest/ConstantTest.cs b/test/TensorFlowNET.UnitTest/ConstantTest.cs
index 588e10d1..cbd1578d 100644
--- a/test/TensorFlowNET.UnitTest/ConstantTest.cs
+++ b/test/TensorFlowNET.UnitTest/ConstantTest.cs
@@ -79,7 +79,7 @@ namespace TensorFlowNET.UnitTest
Assert.AreEqual(result.shape[0], 2);
Assert.AreEqual(result.shape[1], 3);
- Assert.IsTrue(Enumerable.SequenceEqual(new int[] { 3, 1, 2, 1, 1, 3 }, data));
+ Assert.IsTrue(Enumerable.SequenceEqual(new int[] { 3, 2, 1, 1, 1, 3 }, data));
});
}
diff --git a/test/TensorFlowNET.UnitTest/TrainSaverTest.cs b/test/TensorFlowNET.UnitTest/TrainSaverTest.cs
index b7c33e5b..53fab4eb 100644
--- a/test/TensorFlowNET.UnitTest/TrainSaverTest.cs
+++ b/test/TensorFlowNET.UnitTest/TrainSaverTest.cs
@@ -17,6 +17,35 @@ namespace TensorFlowNET.UnitTest
tf.train.write_graph(sess.graph, "/tmp/my-model", "train.pbtxt");
}
+ [TestMethod]
+ public void ImportGraph()
+ {
+ var v = tf.Variable(0, name: "my_variable");
+ var sess = tf.Session();
+ tf.train.write_graph(sess.graph, "/tmp/my-model", "train.pbtxt");
+ }
+
+ [TestMethod]
+ public void SaveSimple()
+ {
+ var w1 = tf.Variable(tf.random_normal(new int[] { 2 }), name: "w1");
+ var w2 = tf.Variable(tf.random_normal(new int[] { 5 }), name: "w2");
+
+ var init_op = tf.global_variables_initializer();
+
+ // Add ops to save and restore all the variables.
+ var saver = tf.train.Saver();
+
+ with(tf.Session(), sess =>
+ {
+ sess.run(init_op);
+
+ // Save the variables to disk.
+ var save_path = saver.save(sess, "/tmp/model.ckpt");
+ Console.WriteLine($"Model saved in path: {save_path}");
+ });
+ }
+
[TestMethod]
public void Save()
{